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5th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021

作者： Sun, Daozhu Li, Nan Yang, Shudan Du, Qiming Information Engineering University Department of Cyberspace Security Academy Henan Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan Harbin China

ISBN: (纸本)9781665415989

In the era of big data, personal privacy leakage has become a hidden danger for social development. As an output privacy protection model, differential privacy can effectively quantify and limit personal privacy disclosure. Combining differential privacy with decision tree algorithm, a decision tree algorithm is proposed to meet differential privacy protection. The algorithm uses exponential mechanism to select split nodes and Laplace mechanism to add noise to leaf nodes. When allocating the privacy budget, the correlation and quantity of the data set are fully considered, which saves the privacy budget and realizes the dynamic change of the privacy budget with the change of the size of the data set. The experimental result on Adult data set display algorithm effectively improves the accuracy of classification result prediction under the premise of privacy protection. © 2021 IEEE.

关键词： Decision trees

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Tuning 2D magnetism in Fe3+XGeTe2 films by element doping

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National Science Review 2022年第6期9卷 163-170页

作者： Shanshan Liu Zihan Li Ke Yang Enze Zhang Awadhesh Narayan Xiaoqian Zhang Jiayi Zhu Wenqing Liu Zhiming Liao Masaki Kudo Takaaki Toriyama Yunkun Yang Qiang Li Linfeng Ai Ce Huang Jiabao Sun Xiaojiao Guo Wenzhong Bao Qingsong Deng Yanhui Chen Lifeng Yin Jian Shen Xiaodong Han Syo Matsumura Jin Zou Yongbing Xu Xiaodong Xu Hua Wu Faxian Xiu State Key Laboratory of Surface Physics and Department of Physics Fudan University Institute for Nanoelectronic Devices and Quantum Computing Fudan University College of Science University of Shanghai for Science and Technology Laboratory for Computational Physical Sciences(MOE) Fudan University Solid State and Structural Chemistry Unit Indian Institute of Science School of Electronic Science and Engineering Nanjing University Department of Physics University of Washington Department of Electronic Engineering Royal Holloway University of London Materials Engineering The University of Queensland Beijing Key Laboratory of Microstructure and Property of Advanced Materials Institute of Microstructure and Property of Advanced Materials Beijing University of Technology The Ultramicroscopy Research Center Kyushu University State Key Laboratory of ASIC and System School of Microelectronics Fudan University Collaborative Innovation Center of Advanced Microstructures Department of Applied Quantum Physics and Nuclear Engineering Kyushu University Centre for Microscopy and Microanalysis The University of Queensland Shanghai Research Center for Quantum Sciences

Two-dimensional（2D） ferromagnetic materials have been discovered with tunable magnetism and orbital-driven nodal-line features. Controlling the 2D magnetism in exfoliated nanoflakes via electric/magnetic fields enables a boosted Curie temperature（TC） or phase transitions. One of the challenges, however, is the realization of high TC2D magnets that are tunable, robust and suitable for large scale fabrication. Here, we report molecular-beam epitaxy growth of wafer-scale Fe3+XGeTe2films with TCabove room temperature. By controlling the Fe composition in Fe3+XGeTe2, a continuously modulated TCin a broad range of 185–320 K has been achieved. This widely tunable TCis attributed to the doped interlayer Fe that provides a 40% enhancement around the optimal composition X = 2. We further fabricated magnetic tunneling junction device arrays that exhibit clear tunneling signals. Our results show an effective and reliable approach, i.e. element doping, to producing robust and tunable ferromagnetism beyond room temperature in a large-scale 2D Fe3+XGeTe2fashion.

关键词： 2D ferromagnetic material Fe3+XGeTe2 film element doping above room temperature, TC tunability

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Association Analysis of User Promiscuous Traffic Based on Convolutional Neural Network 4

Association Analysis of User Promiscuous Traffic Based on Co...

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4th IEEE advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2021

作者： Li, Yonglin Liu, Yan Zhengzhou University Zhong Yuan Network Security Research Institute Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781728185347

Analyzing user behavior from promiscuous network traffic, digging out user characteristics, and analyzing user behavior patterns are the main purposes of user association analysis. Existing methods start from the traffic generated by users and manually select the features that need to be extracted with the help of expert knowledge, the effect of the model depends to a certain extent on the quality of the selected features. This paper proposes a convolutional neural network-based user promiscuous traffic association analysis method Convolutional Neural Networks Based Association Analysis (CNNBAA). First, the traffic generated by users surfing the internet is preprocessed, such as segmentation, anonymization, and an interception, and then mapped into RGB map as the input data of Convolutional Neural Networks (CNN), and then use Residual Networks (ResNet) train the generated images to obtain a network model that can independently learn the characteristics of user online behavior. This method does not require manual selection of features and uses a convolutional neural network to learn features autonomously. We collected the network traffic in the true environment, constructed the experimental data set, and obtained the best traffic segmentation method and the best image conversion resolution through experiments. The experimental results show that the accuracy of the model is above 88%, which achieves the expected effect. © 2021 IEEE.

关键词： Convolutional neural networks

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Augmenting Short Enrollment Speech via Synthesis for Target Speaker Extraction

Augmenting Short Enrollment Speech via Synthesis for Target ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Zikang Lin, Jingru Ge, Meng Jiang, Yu Wang, Xiaobao Wang, Longbiao Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Shenzhen China Department of Electrical and Computer and Engineering National University of Singapore Singapore Co. Ltd. Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9798350368741

A high-quality enrollment speech is crucial to target speaker extraction (TSE), since it provides essential cues for identifying the target speaker in the mixture. However, real applications usually only permit a short enrollment speech, e.g. a wakeup word for a mobile device, that provides limited cues. To address this issue, we propose an enrollment augmentation strategy that allows us to enrich the limited enrollment speech with massive text data through speech synthesis. By doing so, the extended enrollment speech contains enhanced speaker timbre and phonetic content which leads to better extraction quality. Furthermore, we propose a training data augmentation strategy to improve the model's robustness and generalization in short enrollment speech scenarios. Experiments on Libri2Mix demonstrate that our proposed strategies bring a significant improvement in extreme scenarios where only 0.5s and 1-word enrollment speech is provided. We also release our code at https://***/HuangZikang-TJU/Aug4TSE. © 2025 IEEE.

关键词： continue speaking data augmentation short enrollment speech synthesis Target speaker extraction

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Computation Offloading for Multi-user Sequential Tasks in Heterogeneous Mobile Edge computing 17th

Computation Offloading for Multi-user Sequential Tasks in He...

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17th EAI International Conference on Collaborative computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Xu, Huanhuan Zhou, Jingya Gu, Fei School of Computer Science and Technology Soochow University Suzhou215006 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi214125 China

ISBN: (纸本)9783030926342

Currently, many computation offloading studies in Mobile Edge computing (MEC) mainly focus on the multi-user and multi-task offloading, where the tasks are independent and inseparable. However, nowadays many mobile applications, such as Augmented Reality (AR) glasses, face recognition, etc., can be classified into sequential tasks. Dependencies among tasks and resource competition among multiple users in the heterogeneous edge environment make the offloading problem very challenging. In this paper, we propose a new multi-user sequential task (MUST) framework to address the above challenge. Specifically, we present a comprehensive analysis of the time cost of the task offloading process in the MUST framework and define the multi-user sequential task offloading problem. Moreover, we prove the problem is NP-hard and propose a reMUST algorithm based on regular expression to obtain the approximate optimal solution. Numerous experiments have shown that the proposed method is superior to existing alternatives in terms of cost and system scalability. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： computation offloading

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Air Pollution Monitoring By Integrating Local and Global Information in Self-Adaptive Multiscale Transform Domain

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IEEE Transactions on Multimedia 2025年 27卷 3716-3728页

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： Information leakage

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Impulsive Control of Heterogeneous Multi-agent Systems with Saturation Constraint and Time Delay

Impulsive Control of Heterogeneous Multi-agent Systems with ...

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2024 China Automation Congress, CAC 2024

作者： Jiao, Ranran Li, Bo Chen, Xiangyong Jiang, Xiaowei School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China

ISBN: (纸本)9798350368604

This paper addresses the problem of leader-following consensus in heterogeneous multi-agent systems (HMAS) with input saturation and communication delay. Given the practical differences between leader and follower dimensions, we design virtual leaders to address this problem by allowing actual leaders and followers to communicate with virtual leaders separately, thus enabling indirect communication between actual leaders and followers. Additionally, the control protocol proposed in this paper takes into account the effects of input saturation and time delay that exist in real communication. At last, a simulation example is presented to demonstrate the effectiveness of the designed control protocol. © 2024 IEEE.

关键词： Multi agent systems

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Memristive structure of Nb/HfOx/Pd with controllable switching mechanisms to perform featured actions in neuromorphic networks

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Nano Research 2022年第9期15卷 8410-8418页

作者： Junwei Yu Fei Zeng Qin Wan Yiming Sun Leilei Qiao Tongjin Chen Huaqiang Wu Zhen Zhao Jiangli Cao Feng Pan Key Laboratory of Advanced Materials(MOE) School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China Center for Brain Inspired Computing Research(CBICR) Tsinghua UniversityBeijing 100084China Microelectronics Institute Tsinghua UniversityBeijing 100084China School of Materials Science and Engineering University of Science and Technology BeijingBeijing 100083China

All memristor neuromorphic networks have great potential and advantage in both technology and computational protocols for artificial *** is crucial to find suitable elementary units for both performing featured neuromorphic functions and fabrication in large *** a simple memristive structure,Nb/HfOx/Pd,is proposed for this *** two resistive switching mechanisms,Mott transition of NbO2 and oxygen vacancy(Vo)migration,can be controlled by modulating external bias *** bias activates reversible phase transition and restrains Vo filament formation to allow the memristor to mimic the firing action *** bias activates Vo filament formation and restrains the other to allow the memristor to mimic synaptic plasticity and learning *** system can respond adaptively to naturally generated action potentials and modified synaptic signals from the same memristive *** addition,some special features related to signal encoding and recognition are discovered when the system is settled according to chaos circuit *** study provides a novel approach for designing elementary units for neuromorphic computations.

关键词： memristive system phase transition oxygen vacancy action potential synaptic plasticity

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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A Note on "Constructing Bent Functions Outside the Maiorana-McFarland Class Using a General Form of Rothaus"

arXiv

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arXiv 2025年

作者： Guo, Fei Wang, Zilong Gong, Guang State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou450001 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

In 2017, Zhang et al. proposed a question (not open problem) and two open problems in [IEEE TIT 63 (8): 5336–5349, 2017] about constructing bent functions by using Rothaus’ construction. In this note, we prove that the sufficient conditions of Rothaus’ construction are also necessary, which answers their question. Besides, we demonstrate that the second open problem, which considers the iterative method of constructing bent functions by using Rothaus’ construction, has only a trivial solution. It indicates that all bent functions obtained by using Rothaus’ construction iteratively can be generated from the direct sum of an initial bent function and a quadratic bent function. This directly means that Zhang et al.’s construction idea makes no contribution to the construction of bent functions. To compensate the weakness of their work, we propose an iterative construction of bent functions by using a secondary construction in [DCC 88: 2007–2035, 2020]. © 2025, CC BY-NC-ND.

关键词： Hadamard transforms

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